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The term epigenetics refers to typically heritable biological mechanisms which facilitate stable yet reversible modifications of gene expression or phenotype state, without alteration of the underlying genetic code. More specifically, epigenetic mechanisms allow organisms to control which genes are active at a given time. In eukaryotes, epigenetic mechanisms have essential roles in gene regulation, cellular differentiation and genetic packaging. These epigenetic mechanisms give rise to functionality which DNA alone is generally incapable of providing.This thesis takes inspiration from the fields of genetics and epigenetics, and builds a computational model which captures the beneficial properties of epigenetics in silico. This computational model is referred to as the artificial epigenetic network. The artificial epigenetic network can dynamically control which genes within the network are active at a given time, allowing certain groups of genes to become specialised towards specific aspects of a task.Hence, the artificial epigenetic network can contain many different regulatory circuits, each with specific properties. This gives the networks the ability to more readily express a wider range of dynamical behaviours, which were found to produce a number computational benefits. The artificial epigenetic network is applied to a diverse range of control tasks, each with varying dynamics, to ascertain how the functionality of the artificial epigenetic structures effects the functionality of the network. An emergent property is that the epigenetic structures can partition the network into functional units corresponding to the logical decomposition of the tasks, and control these units with a switch like behaviour. This provides an interface, where a user can gain control over the complex dynamics of the target domain via the activation or deactivation of these switches.3

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